Triple
T9077160
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Figline Valdarno |
E217516
|
entity |
| Predicate | distanceToFlorence |
P69982
|
FINISHED |
| Object | about 30 km southeast of Florence |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: about 30 km southeast of Florence | Statement: [Figline Valdarno, distanceToFlorence, about 30 km southeast of Florence]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToFlorence Context triple: [Figline Valdarno, distanceToFlorence, about 30 km southeast of Florence]
-
A.
distanceFromFlorence
chosen
Indicates the spatial distance between a given entity and the location of Florence.
-
B.
distanceToRome
Indicates the spatial distance between a given entity and the location of Rome.
-
C.
distanceFromRome
Indicates the measured spatial distance between a given entity’s location and the city of Rome.
-
D.
distanceFromMilan
Indicates the spatial distance between a given entity and the city of Milan.
-
E.
distanceToVenice_km
Indicates the physical distance, measured in kilometers, between a given place and the city of Venice.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69ca83d6c14c8190bc056d927f00a2a2 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cc95c6a67c81908d143aa25e1e803e |
completed | April 1, 2026, 3:49 a.m. |
| PD | Predicate disambiguation | batch_69cc65fa79bc81908b46f05c8bba920f |
completed | April 1, 2026, 12:25 a.m. |
Created at: March 30, 2026, 7:12 p.m.